Therefore we also implemented type-constrained decoding (Täckström et al., 2013), i.e., prune the lattice such that the tweet words observed on the website have one of the tags they were labeled with on the website (soft constraints), or, alternatively, were forced during decoding to have the most frequent tags they were labeled with (hard constraint decoding), thereby focusing on licensed sequences.

we exploit the fact that tweets with URLs provide a one-to-one map between an unlabeled instance and the source of supervision, making this unlabeled tweet-website pairs 3: I iterations 4: k = 1000 pool size 5: v=train(X) base model 6: for i ∈ I do 7: v=train(X) 12: end for 13: return v Figure 1: Semi-supervised learning with not-so-distant supervision, i.e.


With these important extensions, our model of NLU is highly driven by the semantic meaning representation that is being built incrementally for the utterance.

we leave important aspects, such as negation in an utterance, to future work (they were not very common in our data).


This is not surprising if we consider the high predictive power of ASR (glass-box) features that, when used in isolation, lead to a considerably lower MAE with respect to the other three groups.

overall, also in this more complex scenario where the glass-box features cannot be used, our results demonstrate a good prediction capability of the QE models, which are still able to beat a strong baseline.


indicates the position from where a component is dislocated, while '$' indicates the dislocated component.

discuss a multi-layered representation framework for Hindi and Urdu, where the information from syntactic as well as dependency parse is presented together.


So it was natural to treat these as two words, in the constituency trees.

let us look at another example, where the ambiguity in morphological analysis has led to the ambiguity in the readings resulting in the disagreement in the parse.


This is a type of discourse-level uncertainty, more specifically, it is called weasel (Ganter and Strube, 2009).

subjectless sentences are a typical strategy in Hungarian to express sourceless statements (weasels), but the subject can be deleted due to syntactic ellipsis as well, thus distinguishing between subjectless sentences that denote uncertainty and those that do not is a special task in Hungarian uncertainty detection.


• Syntactic features: for each word, its dependency label was marked.

there are domain differences in the results.


drawing from a harmonized POS tagset (Petrov et al., 2012), transfer models have been used for a variety of languages.


2013, we can see that they are comparable with some differences that are due to the tools and learning parameters they apply which are along the lines of Zhang and Nivre (2011).

it is known that these heuristics focus on recall and tend to add many additional links that may not be useful for our projection task and even lead to some confusion as depicted in the example in Figure 3.


In depth comparison: our SCCM deals with both consituents and distituents, and contexts of them, while PIALIGN only deals with constituents.

one of the non-parametric Bayesian priors, the Pitman-Yor-Process (PYP) prior, is defined on α ij,pq |B ij,pq .


We ran experiments on CTB5.0 using the standard data allocation: files from CHTB 001.fid to CHTB 270.fid, and files from CHTB 400.fid to CHTB 1151.fid were used as training set.

at the meantime, a-6 and a-7 still remain.


Comparing to their work, our approach is more delicate, which is splitting the categories according to the knowledge, and learning the knowledge structure according to data during the training course.

the word sets under distinct dividing standards may be overlapping.


The tuple sequence model does not make any phrasal independence assumption and generates a tuple by looking at a context of previous tuples.

the BLEU scores are modestly improved by combining the three factorized RNNTSMs.


We then discuss theoretical reasons for the empirical results.

furthermore, Kachru's Three Circles of English (Kachru, 1992) raises a question.


Limitations A limitation of our work is that we focused on language variation between persons, and not on variation within persons.

individual players versus an automatic system When considering players with 7 or more guesses, the average accuracy for a player is 0.71.


We find that the difficulty varies widely across users, and that there are no distinct groups of 'easy' and 'difficult' users.

on average, we have 210 guesses per Twitter user, providing substantial evidence per Twitter user.


data N-GRAMS: selected models +opt, -all +opt, +all sc For the more surface-based n-gram (word-and lemma-based), the optimizer selected only up to n = 3, whereas for the more abstract ones (POS-and OCPOS-based), models up to n = 8 were included.

in total NT11 includes 5843 texts, with the following division into languages: Arabic (846), Chinese (1048), French (456), German (500), Hindu (400), italian (467), Japanese (447), Korean (684), Spanish (446), Telugu (200), Turkish (349).


We find that 86.78% of the clusters is in the 1-2 order, a far higher percentage than is generally reported and higher than the Wikipedia corpus (71.65%).

as mentioned, the only constructions exhibiting optionality that we did not include, are the cluster with 'other' verbs instead of auxiliary or modal verbs.


This process avoids subjectivity beyond the definition of the construction.

this particular case of variation allows for a lot of optionality.


We avoid this issue by using an automatically annotated corpus, and can extract large samples of various types of constructions simply by defining what counts as a cluster in the syntactic annotation.

it limits the variables that can be used in the study to the ones that are, or can be, automatically annotated in a corpus.


Figure 6 gives an intuitive sense of how this works.

there is almost a state of anarchy after rebels took power in March.


These were transcribed and split up according to the landmark to which they corresponded.

note that the number of utterances with a pointing gesture is still 370 in the pruned dataset.


those squares) or an identity expression (variable ID; e.g.

this does remain the next best combination for J48, consistent with the results on the complete dataset.


We also tested one of the best combinations involving descriptive features and Distance but excluding the Deictic feature from the set of descriptive features.

they find no impact of focus shifts on pointing gestures; our results, by contrast, suggest that such shifts do play a role.


Although not reflected by standard quality metrics (see next subsection), a wrong hyphenation usually is eye-catching for human readers, which gives rise to a desirable further quality improvement that should be aimed for in future work.

the producibility of the first letter is critical for the overall success: we observe an almost 90% success rate for the self-referential acrostics compared to the about 20% for all others.


Search algorithms maintain so-called OPEN-and CLOSED-lists to manage the exploration status of generated nodes.

as for the acrostic use case, the resulting text's quality gives the most obvious possibility for improvements.


The experiments show that many acrostics based on common English words can be generated in less than a minute.

since our approach is the first attempt at the problem of acrostic generation, we cannot compare to other systems from the literature.


To render different problems and solutions comparable, the achieved quality of a solution T * has to be normalized.

we see our main contribution in the presented technology paradigm: a novel and promising combination of methods from Natural Language Processing and Artificial Intelligence.


the development set is the NISt 2005 test set and the test set is the NISt 2006 test set.


We believe that translation option graph has three advantages over search graph: 1.

the training set is used to train the phrase-based translation model and language model for Moses (Koehn et al., 2007).


For example, Figure 1 is actually a search graph and many phrases occurs multiple times in the lattice (e.g., "and" and "Sharon").

a query lattice is a compact representation of exponentially many queries.


Then a vector (dis r 1 , dis r 2 , ..., dis r n−1 ) is obtained.

d n is the set of dep-ngrams with the length of n. len h is the length of the translation.


We can also find all the same three underlined words in the translation with the same order as they appear in the reference.

fscore is calculated by formula (5), where α is a value between 0 and 1.


To test the first hypothesis, we investigate the role of parser accuracy in QE.


(i.e., a Hiero rule X → 在 X 1 后, after X 1 ).


In the latter setting, the proliferation of clean knowledge bases with rich semantic relations between Web entities, e.g., DBpedia (Auer et al., 2007), Freebase (Bollacker et al., 2008), or YAGO (Suchanek et al., 2007), has given rise to novel, reliable NEL techniques (see Section 2) that exploit the semantic relatedness between entities for the linking process (Shen et al., 2012;Hoffart et al., 2011b;Hoffart et al., 2012).

since the bagging strategy is mainly affected by contextual information, we turn off the component responsible for the "prominence" score and run BEL 10 times for each bagging size on these articles.


Finally m is linked to the candidate that is ranked as the top entity by the majority of the ranking classifiers.

the bagged language models have a high impact on the final decision in cases where the occurring mentions are highly ambiguous but contain valid key information surrounding the mention.


We evaluated the user-created extractors against the gold standard annotations.

knowing only that relations should hold between entities of type PERSON and entities of type EDUCATIONAL INSTITUTION, the user starts an exploration process by providing only these entity type restrictions.


The data confirm our intuition that temporal information is a strong constraint when detecting causal relations, with the BEFORE class having the most overlaps with CLINK and AFTER with CLINK-R.

for example, in the sentence "StatesWest Airlines withdrew T its offer to acquire Mesa Airlines because the farmington carrier did not respond S to its offer", the dependency parser identify because as the mark of acquire instead of withdrew.


To predict types of each entity mention, we first use search engines to expand entity mentions.

distant supervision (also known as weak supervision or self supervision) is used to a broad class of methods in information extraction which aims to automatically generate labeled data by aligning with data in knowledge bases.


Although MLU values may not be meaningful across languages, the general approach is suitable for analysis within different languages.

this could be used, for example, in diagnosis of language impairment.


Table 5 shows the contribution of RS on seed pattern generation and we report the result of Chinese event extraction which only uses the seed patterns 5 .

among all 33 event types, the performances of almost all event types associated with justice are higher than other event types for their unambiguous definitions and high coverage of seed triggers while event Transport achieves the lowest performance for its complexity and low coverage of seed triggers.


Within computational linguistics, the continuous lexical attribute that has received the most attention is undoubtedly the positive-negative spectrum, otherwise known as semantic orientation (SO) or polarity.

in order to proceed with a supervised approach, we need a ranking of a set of words relevant to the lexical attribute that we wish to acquire; this ranking is specific to the attribute in question, so we discuss this in later sections.


In this paper we report on our efforts to extract semantic relations essential to the analysis of discourse and its interpretation, in which links are made between units of text or rather their semantic representations as in (1) in virtue of semantic information about the two main verbs of those clauses.


We note that a high proportion of the implicit cases contains verb pairs that have been collected in a marked context, even for rarely marked relations like elaboration or continuation-contexts with these relations are the majority in Annodis.

6 Finally, we investigated a measure that evaluates the contribution of each element in the triple to the significance measure (this measure is similar to specificity).


In this paper we address the question of the reliability of the predictions made by MT Quality Estimation (QE) systems.

we have also shown that it is possible to improve the detection of the best/worst sentences by altering the distribution of the training set; however the question whether this can be achieved while maintaining a decent level of global performance remains open.


Indeed, the set of Quest features that we use contains many features which depend only on the source sentences.

it is also useful to look at the sorted scores, but with their corresponding predicted score (for the same sentence) plotted on the same x coordinate; this what is shown on figure 4, for the wmt13a dataset only (because the phenomenon is the most accentuated in this dataset, and scores conveniently belong to [0, 1]).


When accuracy and relevance for the task improves, the modified graph or activation strategy identifies portions of interest for further development.

an advantage in this case however, is the different semantic categories involved: the classes of Cat and Scan.


Using an SVM, we might expect to be able to effectively learn which of these features should be ignored and which should be combined, to decide the correct direction of entailment in the majority number of cases in our training data.

lee (1999) noted that the substitutability of one word for another was asymmetric and proposed the alpha-skew divergence measure, an asymmetric version of the Kullback-leibler divergence measure.


(2013), that detects triggers, arguments and their roles.

table 2 also shows the performance of the other biomedical event extraction systems introduced above in carrying out argument role classification, since such information was provided as "Decomposition" within the results of the original task evaluation 5 .


A further issue is that the test data sets for the official task setting have not been made publicly available.

the GENIA task definition requires event attributes to be predicted, but the ACE 2005 task definition does not.


These changes were made in an attempt to address the two major differences between the GENIA and ACE 2005 tasks, i.e., the simpler event structures and the availability of entity attribute and coreference information in ACE.

instead of non-entity mentions, coreferent entity mentions that are the closest to triggers are annotated as arguments in GENIA.


From the results shown in Figure 5, we can see that our probabilistic Co-Bootstrapping method can achieve state-of-the-art performance on all metrics: Compared with the well-known baseline Google Sets, our method can get a 42.0% improvement on P@200; Compared with the SEAL baseline, our method can get a 35.0% improvement on P@200; Compared with the WMEB method, our method can achieve a 6.2% improvement on P@100 and a 3.1% improvement on P@200.

to resolve the above two problems, this paper proposes a probabilistic Co-Bootstrapping method, which can accurately determine the expansion boundary using both the positive and the discriminant negative instances, and resolve the semantic drift problem by effectively maintaining and refining the expansion boundary during bootstrapping iterations.


(Brants and Franz, 2006) as our expansion corpus.


For instance, due to the lower overlap between categories Fruit and Capital, Apple is not a discriminant negative instance since it provides little information for the expansion boundary determination.

the above three steps will iterate until the number of extracted entities reaches a predefined threshold.


2) Evaluate the quality of the patterns.

given all the pattern-instance relations (i.e., whether a specific pattern extracts a specific instance), the likelihood for an instance e being positive is computed as: where R + is all the patterns which extract e, and Ris all the patterns which do not extract e. I + is the set of all positive instances.


Our method has only one parameter: threshold of P+, which determines the instance's polarity.

the expansion category may change gradually when noisy instances/patterns are introduced during the bootstrapping iterations.


The purpose of this diversification is to appeal to customers with special needs.

if one also considers the entire articles, some notable topical differences between brands and types become obvious.


Following (Globerson et al., 2007), we embed words and their contextual, orthographic, and morphological features in a high dimensional Euclidean space that relates their joint probability to distance.

on multilingual experiments our results are significantly better than or comparable to the best published word or instance based systems on 15 out of 19 corpora in 15 languages.


We have presented a novel approach to the decipherment of monoalphabetic substitution ciphers that combines character and word-level language models.

use beam search for decipherment in their character-based approach.


Direct comparison of the execution times with the previous work is difficult because of variable computing configurations, as well as the unavailability of the implementations.

relate a successful decipherment of a nineteenth-century cipher, which was achieved by combining both manual and computational techniques.


Prior to deriving the word language models, a sentence consisting of a single UNKNOWN token is appended to the training corpus.

in real-life applications, we may encounter cases of imperfect encipherment, in which some characters are incorrectly mapped.


Figure 4 shows the decipherment error rate of the beam-search version of our algorithm vs. the published results of the best-performing variants of Ravi and Knight (2008) and : letter 3-gram and 6-gram, respectively.

we use it to recover transliteration mappings between different scripts without parallel data, and to solve the Gold Bug riddle, a classic example of a substitution cipher.


Our model with the capacity to use both a topic-specific vocabulary and a common vocabulary (with common) produces better segmentation scores, ending up with at least 2.5% better absolute F-scores than its context-oblivious counterpart (baseline).

this suggests that the broader context in which sentences are uttered may help in the word segmentation process, and could presumably be used at various stages of language development.


Various kernels, such as the convolution tree kernel (Qian et al., 2008), subsequence kernel  and dependency tree kernel , have been proposed to solve the relation classification problem.

rNN represents recursive neural networks for relation classification, as  Table 3: Classifier, their feature sets and the F1-score for relation classification.


The labeling f (x) is thus defined as: f (x) = arg max y∈Y + F (x, y; w).

a normalization step is applied to each post: fully capitalized words are converted in lowercase; reply marks are replaced with the pseudo-token USER, hyperlinks by LINK, hashtags by HASHTAG and emoticons by special tokens 5 .


from the heads in the stack and from the words in the queue.

we provide comparative scores of the Berkeley parser (Petrov et al., 2006), of the current highest performing single parser on French SPMRL data (Durrett and Klein, 2015), of the manually chosen model in (Crabbé, 2014) and of the model selected by FwRAP (Section 5.4).


In the case of parsing, weak learners can be seen as weak parsers, trained each on a very small set of templates.

this is not ideal for a parsing model; time efficiency at parse time is important and, depending on the number of weak parsers available, a model selected with this stopping criterion could be huge, and therefore slow at test time.


Although not the primary focus of this work, we find that SMTL and MTL can be significantly improved in low resource setting through weighted sampling of tasks during training.

as standard in multi-task learning, the weights at the output layer (g) are task-specific in both settings.


To verify the second issue, we consider a low resource setup following Duong et al.

we consider the DE, ES, FR, PT, IT and SV universal treebanks as target treebanks, and the EN treebank as the common source treebank.


Strategy 2: treating RASR as two models that fit f (s) and f (s|S) respectively.

later lin and Bilmes propose a variant of MMR framework which maximizes an objective function that considers the linear trade-off between coverage and redundancy terms (lin and Bilmes, 2010;lin and Bilmes, 2011).


Centroid-based methods evaluate the sentence centrality as its importance (Mihalcea, 2004).

to our knowledge, the latter are firstly incorporated into a regression model in this paper.


It is created from a collection of phrases in the student responses, including "The central limit", "central limit teorem" (a typo by the student), "CLT" (its abbreviation), and "Central Limit Thm" (another abbreviation).

we use an ensemble of the above similarity metrics by feeding them as features in a SVM classification model, assuming it will be better suited for this task than the LSA alone.


2 These pairs are then grouped together into four different groups: timex-timex (T-T), event-DCT (E-D), event-timex (E-T) and event-event (E-E).

3 we assign a label whenever a temporal preposition establishes a dependency path between an event (E) and a timex (T), in which T acts as the temporal modifier of E. For example, if T is introduced by a temporal prepositions expressing a STARTTIME sense such as from or since, the relation is labelled as BEGUN BY.


For example, the word, "fever" for nowcasting, and the word, "injection" for forecasting.

most systems have simply aggregated counts of daily flu-related tweets to obtain the current patient status (Aramaki et al., 2011;Collier et al., 2011;Chew and Eysenbach, 2010;Lampos and Cristianini, 2010;Culotta, 2013;Paul et al., 2014).


The values for the two other predictiveness indicators, Mean Absolute Difference and Mean Squared Difference can be found are nearly identical to the Spearman rank coefficient.

for the SemEval 2012 shared task on Semantic Textual Similarity (Agirre et al., 2012), 88 different STS systems were submitted by the participants.


The focus of this paper was establishing the need of alternative evaluation measures besides Pearson correlation and how to assess the quality of intrinsic evaluations.


chrF3 is a character n-gram metric, which has shown very good correlations with human judgements on the WMT2015 shared metric task (Stanojević et al., 2015), especially when translating from English into morphologically rich(er) languages.

similarly, BabelNet (Navigli and Ponzetto, 2012) aligns the lexicographic knowledge from WordNet to the encyclopaedic knowledge of Wikipedia.


Since most of the current non-English wordnets use the Princeton WordNet as a pivot resource, concepts, which are not in this English lexical resource cannot not be realized or aligned to it.

for the second sense, i65336 'a tube in which a body fluid circulates', we assume that we know the German translation for this sense is Gefäß.


With this approach they improve the translation quality over the usage of large generic translation models.

we assume that we have a multilingual parallel corpus consisting of sentences, x l i in a language l, grouped into parallel translations: we also assume that we have a collection of wordnets consisting of a set of senses, w l ij , grouped into synsets, for each language: we say that a context x l 0 i , in language l 0 (in our case this is always English), is disambiguated in n languages for a word w l 0 jk if: a context is disambiguated in n languages for a word, if for each of its translations we have a context in the parallel corpus that contains one of the known synset translations.


We use the standard NEWS 2012 dataset (Zhang et al., 2012) for transliteration.

given n views, we require n C 2 parallel datasets to build systems to convert from any source view to any target view.


In response to such low-resource scenario, four main strategies have been considered.

this collection of treebanks contains more languages than were previously available.


After filtering non-projective trees, we retain the one-best sequence (if one projective tree exists), or the original sequence otherwise.

where preordering aims to find an optimal (for SMT) permutation of source words for each source sentence, our objective is less ambitious, as we only intend to 'fix' a sufficiently large number of divergent patterns between the source and target languages, so as to increase the effectiveness of transfer at the model level.


Here is an example of the final character sequence representation for the word pair vsfuret -ausführt '(he) leads out': (1) v s f u r e t a u s füh r t A consequence of this approach is that our labels cannot only be characters, but also combinations of characters (such asüh in the example above); our label set is therefore potentially unbounded.

for the other methods, adding the 10,000 randomly selected samples to the training set actually decreases the average accuracy, by −0.4 pp for Norma and −2.0 pp for CRF.


In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam.

in the first stage, a convolutional neural network is used to produce sentence representations from word representations.


Each index is determined probabilistically using SBP (Fig.

since the lexicon acquisition includes a kind of segmentation problem, fillers and hesitations may cause mis-segmentations.


4 for clean sentences and hesitation included sentences.

the context information in RNN might be  Figure 4: Weakness of segmental context model suffered from the contiguous noisy words that were caused by the combination of the noise word and OOVs, "<unk>", in the artificial noisy data, and RNN degraded prediction accuracy for artificial noisy data.


We score sequences using the function To find the best parse sequence, we predict the output sequencê We learned the parameters of the CRF as part of our neural architecture, then decode using the Viterbi algorithm to compute the best analysis sequence.

this dataset used a different tagset than the pymorphy2 analyzer (Oflazer, 1994), which is based on OpenCorpora data.


(2016) 94.24 94.5 7-layers + skip output + gating 94.51 94.67 7-layers + skip output + gating (no char) 94.33 94.45 7-layers + skip output + gating (no dropout) 94.06 94.0 9-layers + skip output + gating 94.55 94.69 Table 1: 1-best supertagging accuracy on CCGbank.

there is a lack of exploration and analyzing various kinds of skip connections in stacked LSTMs.


Gain scores for A → B → C and C → B → A are different.

the key is to represent text as an entity grid.


But in our systems, only nouns and pronouns are regarded as entities.

it prevents the improvements expected from permutation.


1 with Λ representing the supertags, G representing embedding matrices, and C representing the convolutional neural network layers.

next, we evaluate the models on the full nLG task, including linearization.


The freedom means models may not be penalized based on decisions that don't matter-thus, at the same time, it also mutes the distinctions between classification decisions.

forward-planning in a generation task produces a growing horizon of syntactic choices while shrinking the horizon of semantic goals.


It ranks event mentions based on the temporal relations and then generates summary by extracting sentences that contain salient event mentions.

, the description of concept a can be: 1.Appositive modifier "a, c1, c2.


(2012)), and are therefore good proxies for system summary quality.

to move to another place, the scouting bee selects a random valid summary.


Swarm Intelligence Swarm Intelligence (SI) refers to the collective behavior of decentralized, selforganized artificial systems.

the Swarm Summarizer converges within about a minute for a given topic, while the Genetic Summarizer converges within 2-3 minutes on average 3 .


Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization.

1 ICSI (Gillick and Favre, 2009) is a recent system that has been identified as one of the state-of-the-art systems by Hong et al.


For example, the Jensen Shannon divergence (JS divergence), an information-theoretic measure not relying on human-written summaries, compares system summaries with source documents regarding their underlying probability distribution of n-grams (Lin et al., 2006).

for example, the Jensen Shannon divergence (JS divergence), an information-theoretic measure not relying on human-written summaries, compares system summaries with source documents regarding their underlying probability distribution of n-grams (Lin et al., 2006).


We split the task of semantic decoding into two steps: (i) training a joint model for predicting the dialogue act and presence or absence of slots and (ii) predicting the values for the most probable slots detected in (i).

when evaluating on the DSTC2 test set, a window of 4 (w4), performs slightly better than other window sizes and better than the simple CNN model.


sentence prefixes verb predicted  n that it was not.

we use SRILM (Stolcke et al., 2011) for N-grams on the BwC, for which Chelba et al.


When obtaining a probability distribution for the next word, the cost grows with the vocabulary size.

we use this forward-looking information to condition our models.


As can be seen, the baseline standard models are outperformed for all model types.

no log 10 prob difference is smaller than log 10 (0.5) ≈ −0.3 (cmp.


To represent each input token, we use four vectors: a learned word embedding w; a fixed word embedding w; a learned POS-tag embedding p; a hand-crafted feature representation d. The four vectors are concatenated together, transformed by a matrix V and fed to a rectified layer to learn feature combination, as where [ w; w; p; d] means the concatenation.

the sequence tagging methods and RNN method have no power of modeling the linguistic structural integrity.


Yuret (1998) has shown that pointwise mutual information (PMI) between words can be used for building dependency structures.

the later philosophical school of Nyāya concentrated on the conditions that make a sentence meaningful and complete for a competent speaker of Sanskrit (Matilal, 1966, 385ff.


For the experiments on TaLAPi corpus, we study the performance in Isolated, Cascade and Joint model.

in this scenario, as Thai SB is to detect word sequence and find the end of each sentence, we consider this to be similar to a sentenceend punctuation prediction task with only two labels.


Most research on SBD focus on languages that already have a well-defined concept of what a sentence is, typically indicated by sentence-end markers like full-stops, question marks, or other punctuations.

in this paper, we have demonstrated for the first time a word-based labelling approach to Thai SBD.


can only be decided based on capitalization, but especially in the social media datasets this signal is not very reliable.

for domains or languages with little training data, accuracy is mainly limited by out-of-vocabulary tokens.


For this step, we use a Support Vector Machine (Joachims, 1998) as implemented in Weka (Holmes et al., 1994), because this sub-task is not easily implemented as a sequence classification task.

they are easily confused with past tense verbs (word class VBD) due to their identical word surface form as in the example below.


The beam search algorithm tests the best candidate after each step for goal fulfillment.

to the best of our knowledge, this is the first joint framework for MRL segmentation and tagging in a transition-based setup.


A short sequence may temporarily be the best candidate and fulfill the goal, while longer (and possibly correct) sequences are incomplete and may be lost.

setup We evaluate the cross-linguistic coverage of our MD models on the UD set.


Learning In the MB model we can access specific information concerning the current node inside the word-lattice.

long sequences have more features, therefore their score may be arbitrarily inflated.


• x * represents the expansion of x by adding more context information.

in this section, we transform the problem of segmenting a word into its syllable units as a problem of sequence labeling task.


Theoretically, paragraph-based representation learning is expected to be more suitable for such tasks as information retrieval, sentiment analysis and document summarization (Huang et al., 2013;Le and Mikolov, 2014;Palangi et al., 2015), to name but a few.

all of the unlabeled reviews are used to obtain the general background information and train the EV model.


More varied data or a more naturally continuous task is expected to fare better for the continuous system.

this task will prove challenging as there is still not many researches underway on DNN-based expressive speech synthesis.


This particularity can be exploited in speech synthesis to model additional information in the speaker models such as speaking styles or emotions (Nose and Kobayashi, 2012;Ling et al., 2013), and it has been used for applications as varied as generating walking motion models (Niwase et al., 2005).

in real life expressiveness present overlap between them because there are not clear frontiers, so being able to take into account those overlaps in the shape of a dynamic synthesizer would be ideal.


Module 3 extracts acoustic values, computes z-scores at available levels, and annotates results as features in each segment.

for prominence labeling only, we include into the match count also partial matches, i.e., words that coincide in one interval boundary or belong to the same prosodic word -as already done in the inter-annotator agreement exercise.


This makes it difficult to reach a satisfactory inter-annotator agreement during the annotation of gold standard annotations and, subsequently, to create consistent large scale annotations.

despite the advances of theoretical studies in speech prosody, so far, no sufficiently large, well-annotated prosody material has been created to support empirical studies and drive the research on empirical techniques for analysis and generation of prosodic cues, especially for application in human-computer interaction technologies.


Moreover, this paper briefly presents an extended Praat functionality for feature annotation to provide easy access and manual revision of the tagger's output as well as retrieval of annotated features at any stage of the computation process.

many of these tools create a set of parallel tiers, assigning different labels to these tiers, and then, output extracted acoustic features in a text format for further processing using other platforms.


In experiments on slot tagging over 17 domains, we demonstrated the effectiveness of our methods by delivering clear performance gains over both naive and strong baselines.

this is also because the amounts of training data are widely different across different domains.


Figure 1 (a) provides an illustration of the proposed architecture The model can be trained by maximizing the log likelihood of correct predictions in the training data: where ans(t) ∈ {1 .

without distinguishing domains, a feature vector takes the form x ∈ {0, 1} d for any domain k ∈ {1 .


We use the sentence aligned ILCI Hindi-Urdu parallel corpora (Jha, 2010) to extract the transliteration pairs.

perso-Arabic on the other hand is not a phonetic script.


Usually word-level transliteration models do not deal with word ambiguity and leave it unresolved.

translation pairs with a normalized score of less than a small threshold of ∼0.1 are considered as transliteration pairs.


We focus on French, first because of the availability of the afore-mentioned DSR, and second because in the French FrameNet corpus (Djemaa et al., 2016) the annotated semantic roles are restricted to essential arguments.

(obj) and (suj) paths represent 42.03% of SSP but 65.89% of DSP (in order to reach that coverage with SSP , the 8 most frequent SSP are needed).


The differences between SSP and DSP are of various kinds, as seen in section 2.2.

switching from surface to deep syntax leads to higher gain for predicted data than for gold data: 5.1 points (56.7 to 61.7) for all trigger, instead of 4.2 for gold data and 6.7 points (61.3 to 68) instead of 6.4 on gold data for verbal triggers.


In addition, much of the work on dependency to constituency conversion relies on dependency trees automatically derived from the Penn Treebank (Marcus et al., 1993) via head rules and assumes that the head-modifier definitions are consistent between the constituent and dependency trees (Xia et al., 2008).

when gold tags are provided as constraints, the tag action accepts the gold tag as the output.


required but non-lexicalized arguments) are directly visible in elementary trees and are handled in parsing mostly by substitution.

interpretation-oriented NLP tasks, such as semantic calculus or translation, call for MWE-dedicated procedures.


We do see a drop in accuracy for all annotators when training on 5 sentences, especially for Student1.

with only one sentence, the parser can learn that a determiner should be the left child of a noun, or that a noun should be the left child of the root predicate.


For the words absent from in the initial parallel corpus, their vector representation is a vector of zero values.

our only assumption is that parallel sentences (source-target) are available and that the source part is annotated.


In practice, to leverage information from rich unlabeled monolingual corpus, we initialize W E word and W IL word with pre-trained word embeddings using word2vec tool (Mikolov et al., 2013).

for example, we will exclude a sentence pair in the first step if the number of names in English is three while only two names are projected to IL.


A major problem of this framework is that it suffers from noises produced by word alignment errors.

but this method will fail if word alignment results are totally wrong.


Second, to combine these two parts of signals, there are connections between two bi-directional LSTMs where the zero step of hidden layers on IL side is initialized by the last step of hidden layers on English side.

we have tag are look-up tables for English/IL word, English/IL character, and English tag respectively.


This shows that annotation is quite consistent within each language.

we train a delexicalised dependency parser based on transfer learning (involving no syntactic annotations for the target language), and train a reranking model based on observed MwEs in only the source language(s).


Most of these works employ traditional technique of using softmax as an activation function on top of a typical convolutional neural network (CNN).

(ii) The set of features is not optimized i.e, optimal subset of features is not extracted, instead complete feature set is appended to the word representations at the input layer.


Expression 3 is a path-type of length one.

the system provides all the possible syntactically valid segmentation and it requires human assistance to choose the relevant segmentations so as to form the semantically correct sentence.


To re-rank the obtained paths, POS-LM gives the perplexity score (Score LM ), which is the geometric average of 1/probability of each sentence, normalized by the number of words.

hard matching between words has long been an obstacle in identifying the relatedness of two sentences (ShafieiBavani et al., 2016b).


Words appearing more than 50 times in the training data were selected as features for training first-step DBN and DAE networks.

automatic category recommendation for given product information helps to solving these problems.


we also developed a new technique which uses an ensemble of multiple classifiers that predicts label paths, node labels and depth-wise labels to decrease classification error.


Examples for both one-sentence queries and multiple-sentence queries are shown in Table 3.

in addition, the work of (Kobayashi et al., 2015) demonstrates that it is reasonably good to use the similarity between the sentence embedding and document embedding for saliency measurement, where the document embedding is derived from the sum pooling of sentence embeddings.


This query-dependent feature, together with the embedding similarity, are used in sentence ranking.

to verify the effectiveness of the joint model, we design a baseline called ISOLAtION, which performs saliency ranking and relevance ranking in isolation.


In addition, the work of (Kobayashi et al., 2015) demonstrates that it is reasonably good to use the similarity between the sentence embedding and document embedding for saliency measurement, where the document embedding is derived from the sum pooling of sentence embeddings.

since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well.


To solve this problem, we previously made our summariser extract full sentences that contain the summary propositions.

while this sentence is not perfectly natural, it verbalises the information about fire-making methods reasonably fluently, without introducing unnecessary information from the source sentence that was not selected for the summary.


In derivation projection, the task is to filter out only the "correct" derivations so we can then train on these.

we have then used cross-lingual supervision to train a semantic parser for Dutch at a far lower cost than the original English one, considering the cost of manually creating explicit syntactic annotation and a semantic lexicon.


Bridging the gap between source and target language does come at an additional cost in performance.

although the data and the formalism are not directly comparable, we note that this f-score is close to those of current state-of-the-art open-domain semantic parsers for English, e.g.


Given such a head word of tag 'X', for the sake of expressive convenience, we tag the corresponding bracket covering all its descendants as 'XC'.

we have defined oracle combination in Section 3.4.


Furthermore, our work concerns very different aspects with (Ren et al., 2013), which uses dependency model for forest reranking of constituent-based parsing.

in Figure 5, we show that the proposed pipeline provides a better trade-off between parsing performance and parsing efficiency.


We proposed to construct composites based on a few existing well-known features to identify similar instances.

it takes as input unlabeled instances and performs feature extraction.


For example, consider the task of argument labeling: Arguments that are syntactically realized as passive subjects are typically labeled A1 1 .

a label is returned only if its associated confidence is higher than a threshold (denoted by θ).


Our model is a a hierarchical bi-LSTM as illustrated in Figure 5.

keystrokes seem to capture mostly structural shallow syntactic information, as confirmed in our experimental evaluation.


The overall accuracy figures are nearly identical, even though the accuracy of the transfered tags is lower.

the multi-source results are, as could be expected, much more robust.


Instead of training ngram probabilities on sentences (i.e., sequences of words), we can train them on words, wherein each word is a sequence of morphemes and morpheme boundaries.

for example, if bookworm is mistaken for a simplex word, English phonotactics would syllabify it as *boo.kworm.


Figure 1(d) gives an example of several 3-step paths containing "Taylor Swift" or "Barack Obama".

(Xiao et al., 2016b) proposes to use a generative model to deal with multiple semantic meanings of a relation.


By contrast, both GRU and CNN have the ability of catching the richer semantic information in short texts, according to the layer-by-layer learning upon the distributed character embeddings.

generally, the architectures introducing CNN or RNN to learn representations of sentences and modeling the relevance of context and candidate response on hyper layers, tend to achieve state-of-the-art performance (Hu et al., 2014;Lowe et al., 2015).


Formally, suppose that BM @N is the top N questions retrieved by the benchmark model, and RS@N is the top N questions retrieved by a method we want to test.

besides the automatically harvested knowledge bases, other types of knowledge such as a traditional Db (Merzbacher, 2002) or a linked open data (Marie et al., 2013) are manually generated and often do not cover new entities, like new idols possibly attracting click-through.


Then, the high value of P (neck|giraffe) indicates that giraffe has an serendipitous property 'neck.'

several approaches (Onuma et al., 2009;Nakatsuji et al., 2010) focus on finding serendipitous items such as funny zombie movie, which is both relevant and unexpected, from user-item matrices.


For example, Probase, mining textual patterns such as "<property> of <category>," may contain 'some part' as a property of a giraffe, which can be recognized as a desirable unexpected property by DM.

we use several criteria such as 'more relevant questions rank higher' based on (Bordino et al., 2013) and evaluate individual dimensions.


Unlike the traditional purpose of the web providing relevant information or answers to user questions, conversely, recent web services ask users unexpected trivia questions to amuse them.

we define a prototype of category c hypothetically as an ordered set of random variables R c = {X a,c |a is a property} (an example distribution of X a,c is shown as the blue dashed line in Figure 2(b)).


Culture can be defined as any characteristic of a group of people, which can affect and shape their beliefs and behaviors (e.g., nationality, region, state, gender, or religion).

first, given a word W , are there significant differences in how this word is being used by two selected cultures?


Corpora of naturally-produced texts such as newspapers and magazines marked with detailed morphological, syntactic and semantic tags, are often used in human language-production research.

within an article, reading times got faster with each screen (screenN) in all measures except for first fixation time.


The rest of the paper is organized as follows.

texts created by psycholinguists exclusively for research purposes, are commonly used in languagecomprehension research.


We evaluate two models with different input representations: • aX + Xb model: This is the flexible frames model of St Clair et al.

this method also lacks satisfactory completeness.


In sum, for most of the verbs in our data set, there is no indicative usage pattern for L1 Chinese compared to the western L1s.

this makes it possible to connect the choices in the language with extralinguistic variables, such as the gender or the age of a speaker.


From studies on language change, we know that diachronically there has been, for example, a shift from a more verbal towards a more nominal style (cf.

in the scientific domain, shared expertise among researchers, for example, will affect language use and give rise to domain-specific language.


Based on a limited context of size n words, the surprisal value of the following word w n+1 corresponds to the negative log-probability: There are two properties inherent to surprisal: (1) units with low probability convey more information than those with high probability, and (2) information conveyed by a unit is crucially dependent on its context.

for POS tagging and syntactic parsing, we use the Stanford NLP toolkit (Manning et al., 2014).


Still, a naive sequence modeler that simply assumed that the poem follows an unstressed-stressed alternation would fare reasonably well here.

the first author's work has been partially funded by the University of the Basque Country (UPV/EHU) in collaboration with the Association of the Friends of Bertsolaritza under the Zabalduz program.


For example, consider the following parallel sentences: ßã,× ã (The root supports the crown, growing the forrest).

• Length difference: The absolute length difference of two sentences.


Moreover, the alignment can occur at various granularity (words, phrases, clauses or sentences).

we adopt machine learning to learn a prediction model based on an annotated dataset.


The typical problems include finding the longest common subsequence (Hirschberg, 1977), longest common substring and multiple sequence alignment (Carrillo and Lipman, 1988;Needleman and Wunsch, 1970).

we start by studying word alignment strategies and look forward to incorporate information from multiple aspects.


Finally, |C(d)| is the number of clauses in the document d, which are defined as a "simple sentences" or, more precisely, the smallest grammatical units which can express a complete proposition 3 .

with this features/dataset combination, with the glm library we see slight rises in Precision@5, F1-Score@15 and MAP from 24% to 24.4%, from 19.22% to 20.30% and from 0.127 o 0.136 respectively; for C5.0, while F1-Score rises from 18.95% to just 19.29%, Precision@5 and MAP shows a more significant improvement from 22.2% to 26.8% and from 0.123 to 0.137 respectively, thus supposedly showing a better ranking of the keyphrases found.


We expected the V av NP construction (e.g.

we used the default settings, except the dimensionality which was set to 512.


Each sequence of alignment operations between two sentences (e.g.

we consider merge consistency a strength of our rule-based approach.


This also means that there is clear limit to the predictability of the phonological system that drives the accommodation of loanwords in Japanese.

*P: /p/ is only tolerated in geminates or partial geminates 2.


The following example illustrates this claim further with three well-known constraints on Japanese words: 1.

studying the loanword phonology matters to linguists who wish to study essential characteristics of the entire phonological system of a language and the tension between the native and foreign lexical strata.


The preposition error detection is one of error cases in NLP-TEA shared tasks.

the ambiguity of preposition selection not only causes confusion to non-native learners, but also makes challenges in natural language processing.


(S1) 在 公眾 利益 方面 來看 (on the view point of public interest) (S2) 從 公眾 利益 方面 來看 (from the view point of public interest) A model trained on an error-annotated dataset benefits from learning the mapping between wrong instances and their corrected counterparts (Cahill et al., 2013).

the n-gram model, even worse, selects 在 (zài, "in") and makes an incomprehensible sentence (S11).


In the 186 prepositions, some of them are abbreviations such as "altho" (although) and "w." (with).

it is feasible to substitute each of all prepositions for the location of a preposition.


(2013) propose a hybrid approach to deal with preposition selection.

the performance gap between L1 and L2 is still an issue to be tackled in the future.


If we were interested in optimizing performance for each language separately (i.e., by using the development set on which we are reporting results as a tuning set), then we would be done now.

this grammar is an implementation of grammar (3) (AG Compounding) from (Sirts and Goldwater, 2013) without the compounding (since the compounding did not perform well for our languages), and we list it among our baselines in the result table.


• The best performing AG-based configuration differs from language to language.

another system was developed by Poon et al.


In this particular example, the verbs were tagged correctly by HunPos, but the neural tagger failed to parse the clause som samma nya tro sagdes predika 'who were said to preach that new faith' correctly.

the overall training objective is a weighted combination of the two error types: to train our model, we apply minibatch stochastic gradient descent with a learning rate of 0.01, together with gradient clipping (Pascanu et al., 2013) to a maximum 2 norm of 10.


An interesting fact about early modern Swedish is that many of its historical features, despite having disappeared completely from the modern form of the language, are still attested in other, closely related contemporary languages.

indeed, both of the adapted Swedish systems in Table 3, while still about 1.5-2.5 percentage points below the performance of HunPos, achieve higher scores on the SUC test set than the tagger trained without the additional constraint.


The dataset contains papers published at the NIPS conference from 1987 to 1999.

we measure performance using the F 1 score (Van Rijsbergen, 1979).


Previous deep neural network-based hashtag recommendation approaches converted the task into a multiclass classification problem.

in this work, to incorporate the histories of users, we propose a novel end-to-end memory network architecture to combine the microblog textual information and corresponding user history to perform the task.


Depending on the cut-off value u, the size of the instantiated transition matrix will be different for every time-step.

alternatively, if the model parameters have ceased to change much between iterations, the learner can be assumed to have found a local optimum.


The AN vectors are built using the multiplicative model of semantic composition (Mitchell and Lapata, 2008).

we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system's performance across individual adjective classes, concluding that a classdependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.


(2013) computed other characteristics of lexical chains and co-reference pairs (such as the number of chains, the distance between entities, the average word length of entities, etc.).

we also computed the ratio of proper names per word (11).


Studies in readability from this period stressed the importance of higher textual dimensions, focusing on inference load (Kemper, 1983), conceptual density (Kintsch and Vipond, 1979), or organisational aspects (Meyer, 1982).

we replicated most features introduced in the literature described in section 2 and added new variables: the proportion of deictic pronouns, of resomptive anaphora and of adverbs, as well as the probability that a specific type of mention might open a co-reference chain in a given text.


Similar to previous studies (François and Fairon, 2012;Dascalu et al., 2013), we found a large effect for this predictor, which completely vanished once word frequency and sentence length were controlled for.

semi-partial correlation will help determine whether our variables contribute to text readability prediction with additional information besides sentence length and word frequency.


Such value is however not unusual in co-reference annotation (Muzerelle et al., 2014).

we present experiments focusing on cohesive features : anaphora chains, reference chains and lexical chains (evaluating sentence similarity).


Hence, we introduce posterior regularization (PR) (Ganchev et al., 2010) into our HMM inference algorithm.

(1) Based on previous work in the area (Tjong Kim Sang and De Meulder, 2003;McCallum and Li, 2003;Sha and Pereira, 2003), we begin with a standard set of features commonly used for training NER systems: given the paucity of training data, these features are numerous and sparse, we do not expect the CRF model to perform well with standard features alone, even with L1 regularization.


As an example, consider the following entities: Beijing, Pékin, and Pequim as expressed in English, French, and Portuguese respectively.

table 2: Characteristics of the generated PropBanks used for training the SRL models and the FrameNets used for evaluating the trained models We evaluated the quality of the generated PropBanks in a practical setting as well as the effectiveness of using entity-like LUs in addition to entities.


Most previous approaches have used professionally translated parallel corpora, mainly EuroParl (Koehn, 2005) and United Nations Corpora (Rafalovitch and Dale, 2009), to transfer semantic annotation.

for Swedish, using entity-like LUs, we observe an improvement of the labeled f1-measure by 10%.


Most previous work relies on word alignments or uses bilingual dictionaries to transfer the predicate annotation between languages.

entities have the property of being uniquely identifiable across languages on a global scope.


Santos and Zadrozny (2014) train character embeddings for boosting their short text classification system based on CNNs.

(2013) employ a graphical model to reconstruct the word forms in Proto-Austronesian using Swadesh lists.


Additional instructions are given to annotators: Each reference summary should be no more than 300 words; and the discriminative sentences should enable the judgment of which group the paper belongs to.

⑤In generating a summary, we use MMR (Maximal Marginal Relevance for multidocument) to avoid redundancy in a summary.


The coherence scores are calculated within two reference corpus: the English Wikipedia (Wiki) and the original dataset (Intra).

the summary by dTM-Dirichlet covers more background group-specific words, like 'summarization' and 'MDS'.


They may add discourse connectives and conjunctions, such as after, however, or as well as.

step 2: selection of best nuggets Redundancy is a more severe issue in MDs (Goldstein et al., 2000b;Goldstein et al., 2000a) compared to single-document summarization, as recurring facts are more likely to occur in multiple different documents about the same topic than within a single one.


Pairs are divided into four categories by source: (1) fragments that come from different documents; (2) fragments that come from different sentences within a same document and that do not contain coreferent entity mentions; (3) fragments that come from different sentences within a same document and that contain coreferent entity mentions; (4) fragments that come from the same sentence.

this ensures that clustering the fragments back into the original corpus has a collocation score of 1.


Previous work considered sentence planning to be a rigid succession of three distinct tasks: lexical choice, aggregation and referring expression generation (Reiter et al., 2000).

to evaluate the trade-off between purity (that favours a large number of clusters) and collocation (that favours a small number of clusters), we use the F 1 measure: Purity+Collocation .


Given a pair of fragments, we train a logistic regression classifier that returns the probability that the pair is positive, which we interpret as the similarity φ between the two fragments.

results show that fragments that contain coreferent entity mentions are more likely to be mergeable (see Figure 4).


Similarly, a large body of work exists in sentence fusion (Barzilay et al., 1999;Barzilay and McKeown, 2005;Marsi and Krahmer, 2005;Filippova, 2010;Thadani and McKeown, 2013).

when computing purity, a gold-standard cluster is the union of the fragments originating from a corpus sentence and all fragments that contain coreferent entity mentions with said sentence.


Once on a position in the DA, the probability of escaping the DA back to the initial state is set to 1 − α, that of continuing in the DA to α.

in this work, we propose to use a character-level model which does not suffer from the same sparsity issues as a word-level model, and therefore does not require de-lexicalisation.


We examine the impact of various loss functions in section 4.2.

the algorithm learns whether to select an attribute or generate a word by taking the corresponding action and assessing its effect on the quality of the complete NL.


In addition to answer existence, we also evaluate the correctness of system-generated answers.

the algorithm uses question template T5, and creates a question "What is a result of the fibroblast division not only in the artificial conditions of cell culture, but also in an animal's body?"


Therefore, they may obtain similar sentence pairs effectively.

second, in maximum alignment (Figure 6), correct word alignments such as "genus, genus," "species, genus," "tree, trees," and "kauri, kauri" are found because many-to-one word alignment is searched greedily.


The combinations METEOR Baseline + Vector and METEOR DBnary + Vector further improve correlations with human judgements (+0.001 points in average).

when we combine lexical data with Vector module (METEOR DBnary + Vector), we observe a small increase of the correlation score, in particular when threshold is tuned, which suggests a tunable version of METEOR.


Under traditional agreement-based measures, this singleton annotation would be in the minority, and so likely removed in a typical adjudication step.

in total, the final corpus contained 9 551 documents.


Note higher recall but lower agreement in crowd.

auto-adjudication is poorly equipped to handle exceptional circumstances; further, it is hard to judge its impact without human intervention.


These two corpora are therefore constrained to anachronistic terminology, with the Ritter data also having socially constrained data (i.e., it is only from people active during one Friday evening -September 17, 2010).

social media data focuses on a broad range of popular figures and even personal names not found in the public sphere (like NICOLA).


We live in a dynamic and rapidly changing world: some companies expand their offices all around the globe, while others collapse; people become celebrities overnight and are forgotten only several years afterwards.

the chess meaning of horse also has knight as a synonym but only horse occurs in the dataset, hence an observed variation of 1.


2004because they do not consider the temporal dimension.

systems are not encouraged to focus on the temporal aspect of the task but in reality the same language system is still used for many different situations within a changing world.


Then we can interpret these relations as some kind of hierarchies.

to extract such parts is difficult even for human annotators except for well-trained experts.


Then those keywords, which appear in the granted first claim but not in the rejected first claim, are extracted.

applicants tend to submit multiple claims in one patent with some hierarchical structures of dependencies.


Traditionally, event extraction has focused on monolingual training -typically English (Grishman et al., 2005;Ji and Grishman, 2008;Gupta and Ji, 2009;Liao and Grishman, 2010;Liao and Grishman, 2011;Li et al., 2013;Bronstein et al., 2015), and occasionally Chinese or other languages (Chen and Ji, 2009b;Piskorski et al., 2011;Li et al., 2012;Chen and Ng, 2012;Chen and Ng, 2014).

the ACE ontology contains 35 different argument types, and we also include the NONE label to indicate when there is no relationship between the trigger and the entity.


The last two competitors' methods are binary classification, which removes the Potential class from the original target category set.

in ARC-II, the improvement is not as obvious as the former, mainly due to that the attention mechanism already takes the future steps into account.


The use of these two feature selection methods did not improve the accuracy of any configuration.

our semantic feature set yields better performance than the common linguistic and statistical feature sets and that combining semantic features contributes to the Hebrew VN-MWEs identification task.


A fully connected softmax layer is used as classifier for classification.

rNNs with the above form may suffer from gradient exploding or vanishing problem (Bengio et al., 1994;Hochreiter, 1997) during training when it is trained with the backpropagation through time algorithm (rumelhart et al., 1986;Werbos, 1990;Williams and Zipser, 1995).


Select a task according to a certain probability distribution (explained below).

figure 1 shows the dependency parses for Entity-Destination(e1, e2)  the two sentences.


Further, typological databases are constructed manually using limited resources, and do not contain information on the distribution of feature values within a given language.

early studies, focused on word order, simply classified languages as SVO (Subject, Verb, Object), VSO, SOV, and so forth (Greenberg, 1963).


The noun and light verb can be scrambled away from each other, but we found this to be fairly rare in the Treebank LVC examples.

they make use of  annotators' linguistic intuition to identify pof cases with a "full understanding that it may lead to some inconsistency in the data" (Bharati et al., 2012, p41).


The biggest differences are that there are no NUM tags in French.

a small part (2,518 tokens equivalent of 5.31% of the French Dundee tokens) of the Dundee Corpus could not be found by manual search in the French Treebank and was therefore omitted from the experiment.


Even though they found that French words (5.2 characters) are on average longer than English ones (4.7 characters), there are more two-letter words in French (19.7%) than in English (17.2%).

figure 3 shows accuracy scores per PoS class comparing experiments with same test set.


Comparing to these neural network based models, our model obtained a comparable performance (the last row of Table  4) without using any sparse features, extra annotated resources and specific training strategies.

it generates the semantic matching vector by selecting the most similar word vector t k from T .


In their model, they just utilized several hand-crafted features in a Support Vector Machine (SVM) model.

we found that the max function worked better than the global function on both MAP and MRR.


To extract features from both the similarity and dissimilarity of a sentence pair, we designed several methods to decompose the word vector into a similar component and a dissimilar component.

it is not necessary that all the semantics of s i (or t j ) are fully covered byŝ i (ort j ).


We conclude our paper and discuss the future work in Section 5.

update the two datasets as follows: + and (ii) • Step 3.


The π t are set to some shared set of sense probabilities.

2012the idea is that these should provide both positive and negative tests for the algorithm.


(2011) also instantiates the pooling option to exploit a time-unaware system.

on these pseudo-neologisms, the proposed model and algorithm has been successful, identifying an emerging 'sense' in an unsupervised fashion.


Supervised, unsupervised, and knowledge-based approaches have been studied for WSD (Navigli, 2009).

vertices for sufficiently similar sentences (based on criteria discussed below) are connected by an edge whose weight is the cosine similarity between the respective context vectors, using the LSTM language model.


We use the French-to-English News Commentary v10 and Europarl v7 corpus from the WMT2015 translation task to train our baseline SMT system.

figure 4 illustrates the gated adaptation mechanisms.


The baseline models are trained on the Penn Treebank only.

instead of learning and adapting the neural network on millions of training sentences-which can be very timeconsuming or even infeasible in some cases-we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead.


We used MADAMIRA (Pasha et al., 2014), the state-of-the-art morphological analyzer and disambiguator, to perform morphological tokenization following ATB scheme (Habash and Sadat, 2006).

we report on Arabic, a language with complex and rich morphology paired with a high degree of ambiguity (Habash, 2010).


Different random initializations force the same training algorithm to converge to different local optima.

the largest improvements for multi-source ensembles are due to our context-dependent combination method.


For the English language model, a 4-gram model with stupid backoff smoothing (Brants et al., 2007) was built and commonly used for all settings.

our system does not depend on specific preordering methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system.


In addition, using reordering models demands a complicated decoding algorithm, in which the decoder has to consider concatenations of source phrases with arbitrary orders.

during word alignment, IBM Model 1 (Brown et al., 1993) and HMM alignment (Vogel et al., 1996) were performed using one-best preordered source sentences and corresponding target sentences.


We chose the best distortion limit of the baseline system for each language pair by the BLEU (Papineni et al., 2002) score on the development data.

it is not trivial to integrating these methods in a single system while comprehending their interactions.


G G B B DFFN-BLNA matches with keywords "tennis","interested,".

we compare our approach with the top two best performing systems from SemEval 2015 -JAIST (Tran et al., 2015) and HITSZ-ICRC (Hou et al., 2015) both of which use HCF based models.


Interestingly, a multi-word verb phrase in English can be expressed with only one noun (e.g., "示威"/demonstration) or a verb (e.g., "游行"/parade) in Chinese.

with the newly learned phrases, we can successfully identify additional civil unrest events in the test data.


The most straightforward idea is executing the projection procedure in Section 3.1 many times.

when we map these already diversified Chinese phrases back to English, new phrases with richer syntactic structures (Figure 1(c)) are generated, including some interesting noun structure phrases and one single-word phrase.


With the newly learned phrases, we can successfully identify additional civil unrest events in the test data.

we cannot see further changes in performance.


Hence, we enumerate a set of Chinese verbs (e.g., 导致, 造成, 引起) whose meaning are "cause" and add them into our connective set CS.

3) Intuitively, tense can clearly identify the temporal relation of two event mentions if they have different tenses.


Take the following two sentences as examples: E2: 埃塞俄比亚与厄立特里亚２３日在这里举行谈判(EV3: Meet)。(The talk (EV3) between Ethiopia and Eritrea will be held on 23 rd .)

their gains are rather small, largely due to the common disconnectedness in the sparsely annotated corpora .


This is because the deep RNN is a large model, which necessitates more data samples for training.

we would like to point out that our data augmentation method is a general technique for relation classification, which is not ad hoc to a specific dataset; that the methodology for dealing with noise is also potentially applicable to other datasets.


Initially, the performance increases if the depth is larger in both settings with and without augmentation.

section 4 presents in-depth experimental results.


The proposed method is trained in an end-to-end fashion.

two kinds of information are used to construct the input representation for each sentence: • Raw tokens: We first split the sentence into a sequence of word tokens, then map each token to a d w dimensional vector called word embedding.


Two Adadelta parameters , ρ = 0.95 and = e −6 , are set with default values.

in the future, we would like to further investigate how different loss functions influence performance, and enrich experiments by carrying out human evaluation as well as making detailed analysis on each relation.


We follow (Zeng et al., 2015) and use a filtered version of NYT10 released by them 1 .

our method is constructed under a neural network architecture, with the merit of no dependency on lexical and syntactic features.


They have a similar problem with ambiguous expressions which they need to solve to determine the correct class (e.g., China can belong to several classes such as LOCATION or PERSON).

for example, we extracted a total of 3968 snippets for the name Michael Jackson (All).


In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation.

one important difference is that we produce trees of varying shapes and depths while the graphs produced by (Cheng et al., 2015) are restricted to trees of depth one i.e., set of DBpedia triples whose subject is the entity to be described.


(2015) presents a convolutional-gated recurrent neural network and applies it to sentiment classification task.

experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.


As the basis of our framework, it is the key to a good performance.

few such researches have been reported on document level in multi-document summarization task.


For fast solvability detection, planning research therefore concentrates on polynomial-time solvable fragments of the plan existence problem.

we design a compilation of that ability into an AI Planning task Π, where unsolvability of Π -the absence of a path to the planning goal -implies infeasibility of e 0 .


In case 1a, "Winter comes" and "It comes" are valid solutions.

in doing so, we will not keep track of the word sequences in edges, and we will not incorporate any notion of word-sequence quality.


In all 21 cases where only the baseline finds a complete solution, k = 4 finds a partial solution.

the search space (following a chart realization algorithm, e. g. (Kay, 1996;Cahill and van Genabith, 2006;Carroll and Oepen, 2005)) traverses collections of partial sentences (edges).


In the following, we summarize and analyze the result of our corpus construction effort.

we also provide a version of the corpus where sentence splitting has already been applied, because most extractive summarizing systems extract sentences.


Since our summaries have different length, we use a variable length parameter l for the evaluation, where l denotes the length of the reference summary.

dUC 2005 offered 10 reference summaries for a subset of the 50 topics in order to cover a more representative sample of the diverging reference summaries.


The annotator with the highest inter-annotator agreement was selected to annotate all the dataset.

here we focus on identifying only the aspect and sentiment for each entity.


Currently there are no dedicated platforms for reviewing and rating aspects of neighbourhoods of a city.

current ABSA task can only recognise that positive and negative opinions towards aspect "service" are expressed.


The unit of text varied from document (Pang et al., 2002;Turney, 2002), paragraph or sentences (Hu and Liu, 2004).

sentiment analysis has applications in almost in every domain and it raised many interesting research questions.


To facilitate research on this task we introduce the SentiHood dataset.

this task assumes only the overall sentiment for each entity.


Instead, we use the word2vec embeddings as the source of the features, as word2vec allows for the computation of similarities between unseen data and training data.

(Buschmeier et al., 2014) compared the performance of different classifiers on the Amazon review dataset using the imbalance between the sentiment expressed by the review and the user-given star rating.


It is counterintuitive, since experimental results prove that both of those features learn almost the same global and contextual features.

when we combine sentiment, emotion and personality features, we obtain 90.70% F1-score.


(2013b) were able to leverage a simple mapping strategy between word embeddings created from large monolingual datasets in order to fill the gaps in translation dictionaries.

in this paper we focus on CLSA at aspect-level 1 Document-and sentence-level CLSA assume that an entire section of text expresses one sentiment towards one entity.


This is supported by its outperforming other models though no oversampling technique is applied to the UTCNN related experiments as shown in this paper.

based on this finding, in the final UTCNN setting we train two user matrix embeddings for one user: one for the author/liker role and the other for the commenter role.


With the transformed word embedding feature, SVM can achieve comparable performance as SVM with n-gram feature.

here we apply three convolutional layers on the concatenated transformed word embeddings  xc w is the word embedding of word w in the comments; U k and u k are the moderator matrix and vector embedding for moderator k; T j and t j are the topic matrix and vector embedding for topic j; R i and r i are the commenter matrix and vector embedding for commenter i.


Still, it has not been shown how several strategies of diverse complexity can be autonomously managed by artificial agents.

all experimental results have been tested on ten runs, to ensure the consistency of the results.


An example for English would be to use a term as "green" to describe a colour sample.

agents classify again the colour sample on the transformed colour space, obtaining a second colour prototype 5 .


This is caused by different membership categories no longer used but still in the lexicon of some agents.

the results obtained show that a population of agents equipped with an architecture of flow successfully manages to progressively develop its communicative skills when trying to remain in a state of flow.


The combination of all the proposed features yielded 0.8703 in total accuracy with RF, which is considerably more accurate than 0.7642 with NN.

• dirRel also exploits the network structure of PWN, attempting to mimic the notion of feature inclusion: "an object with many features is judged as less similar to a sparser object than vice versa" (Gawron, 2014).


Reports from as early as in (Boyd-Graber et al., 2006) showed that major similarity measures could not reproduce the evocation ratings well: the best result reported in the literature was as low as ρ = 0.131 (ρ: 43,459 no-evocation (original) 9,715 no-evocation (incorporated from PWN) 90,058 Total 428,790 the Spearman correlation coefficient), which was achieved with semantic vectors derived by applying the Latent Semantic Analysis (LSA) method (Deerwester et al., 1990) toward the British National Corpus.

(Kotlerman et al., 2010) and (Gawron, 2014) make use of dependency parses acquired from textual corpora as features.


This dataset has been widely used and hence allows the comparison of SubIMDB against state-of-the-art solutions for the task.

the corpus was extracted from 38,102 subtitles of family, comedy and children movies and series, and is the first sizeable structured corpus of subtitles made available.


The scores support the hypothesis in (Burgess and Livesay, 1998): the Pearson correlation for high frequency words peaks at 10 million words, while the correlation for low frequency words continuously grows from 10 to 100 million words.

these resources often suffer from grammar errors and misspellings, excessive use of acronyms and shortenings, partly due to the constrains of the publication means (e.g.


For prompt adherence, at least the errors produced by the ADU compositions (a 3 ) are close to those of c 1 and c 2 , which is why we additionally tested a 3 in combination with the standard feature types.

our results for persuasive student essays underpin the benefit of argument mining, revealing that the mined argumentative structure is particularly helpful for structure-related quality dimensions: Without putting emphasis on the content of arguments, we have improved the state of the art in scoring an essay's organization and even in scoring its argument strength.


Unlike all mentioned approaches, however, we analyze the output of argument mining.

in order to ease the global interpretation of our results, we also report on the majority baseline.


Classical essay scoring often focuses on grammar, vocabulary, and similar (Dikli, 2006), partly employing structural features like discourse markers (Burstein et al., 1998).

"Wouldn't it be better if imagination makes the world go round".


Lemmatization is the task of deriving the lemma from an inflected word form such as atmest→atmen.

we address this by first aligning input and output sequences as exemplified in S l u t l e r f i m S t u d ∅ e r f i rn This alignment matches up character subsequences from both strings.


Aside from fact triples, external information is employed to improve KG embedding such as combining text (Riedel et al., 2013;Wang et al., 2014a;Zhao et al., 2015), entity type and relationship domain Chang et al., 2014), relation path (Lin et al., 2015a;Gu et al., 2015), and logical rules Rocktäschel et al., 2015).

to capture the temporal order of relations, we further define a temporal evolving matrix t ∈ R n×n to model relation evolution, where n is the dimension of relation embedding.


Second, TS significantly impacts the performance of TKs by feeding them with better representations.

note that the attention model may assign different weights to identical words that appear more than once in the sentence.


Convolution-maxpooling slides a window over the input and identifies the best matching subsequence for a task, using a number of filters that support flexible matching.

simpleQA was first investigated in (Fader et al., 2013) through PARALEX dataset against knowledge base Reverb (Fader et al., 2011).


(2015) have proposed an algorithm called scheduled sampling to improve performance of recurrent networks on sequence-to-sequence labeling tasks.

lSTM networks trained with our approach and the approach of (Gal, 2015) yield a lower bit-per-character (bpc) score than those trained with forward dropout onlyWe attribute it to pronounced long term dependencies.


In another effort, (Zhu et al., 2006) propose TagLDA, where they replace the unigram word distributions by a factored representation that is conditioned on the topic and the part-of-speech tag of a term.

to sample from the Archimedean copulas, we rely on the algorithm proposed by (Marshall and Olkin, 1988), which was further improved in (McNeil, 2008;Hofert, 2011) and implemented in the R language .


These models can down-weight the annotations of workers who often disagree with others and up-weight the annotations of workers who often agree with others.

most crowdsourcing models extend the item-response model of Dawid and Skene (1979).


In these methods, hierarchical phrases rather than flat phrases are employed.

first judge whether the two phrases conform to the complete subtree constraint.


In order to use the syntactic structure of the source sentence to guide the prefix extension, we analysed the nature of the prefix and propose two subtree matching rules (or constraints) which should be followed while selecting translation hypotheses.

the synchronous grammar in hierarchical phrases has no linguistic meaning, and cannot evaluate the reasonableness of the hypotheses from the view of syntactic structure.


The English training data is tokenized and lowercased using scripts in Moses (Koehn et al., 2007).

the decoder can then use the topic knowledge of previous translated words to increase the likelihood of selecting words from the same topic.


Simultaneously minimizing L and distribution mean dissimilarity L m is effective.

same to that in the CBOW model, W and M are still K × V and V × K matrixes.


Finally, we present some conclusions and perspectives about this work in the last section.

figure 4: Performance of the adjusted Okapi and word2vec models trained on the AQUAINT-2 corpus; for comparison purpose, the results of the GoogleNews word2vec model are also reported.


Conversely, a large number of dimensions does not allow the necessary generalization needed for retrieving new documents (IR MAP) or the large variety of relations used in WN+M.

we also add the results obtained on the lexical substitution framework proposed in SemEval 2007 (McCarthy andNavigli, 2009).


Of course, such embedding techniques rely on the same assumption than "traditional" distributional semantics (although many studies do not acknowledge it clearly).

for these evaluation scenarii, a dimension between 300 and 500 appears as a good compromise.


An interesting property of the representations learned by SG, CBOW and GloVe is that they capture similarity as well as analogies and related linear relationships.

second, context words j for which x ij = 0 are completely ignored in the GloVe model.


As can be seen from the figure, the correlation between these two quantities is very weak, e.g.

following (Pennington et al., 2014), we have removed punctuations, lower-cased the tokens, removed HTML/XML tags, and conducted sentence segmentation.


For each mention, we set the maximum number of its context mentions as 50 5 .

lSTMs contain special units called memory blocks in the recurrent hidden layer.


Note that we repeat the linear transformation of the state cell of mention m i (i.e., W m i h m i ) t times.

mediated/function and mediated/bridging benefit most from word embeddings which provide useful semantic knowledge to capture these two categories.


Since the relations form a hierarchical discourse structure, the arguments for relations higher in the hierarchy or lower in the hierarchy could span over ranges of various lengths.

vECTOR: This feature set is built using word embeddings.


CONNECTIVE: This feature set contains connective related information, including the string of the connective and the number of connective components it has.

sUBJ: The sUBJ feature is set if a segment contains a subject.


Our results indicate that our architecture learns useful representations capturing some of the syntactic and contextual information needed for the task.

none of them considered multi-task learning architectures, however.


Note, however, that in a sequence prediction model we have no guarantee that our output structures form well-formed discourse trees.

moreover, factuality (Fact), aspect (Asp), modality (mod) and polarity (Pol) information prove to be useful for discourse parsing.


All the auxiliary tasks are, as the main one, document-level sequence prediction tasks.

the PDTB contains much more data than the RST-DT, with more than two thousands documents annotated against around four hundreds in the RST-DT, making it interesting to try to take advantage of this relatively large amount of discourse annotated data.


The results of the evaluation for reliability are encouraging and indicate that the method is suitable for the systematic analysis of non-cooperation.

for the segmentation and dialogue act annotation stage, four annotators (one of the authors and three native English-speaking researchers with previous experience in dialogue annotation) received transcripts of the corpus and were asked to segment the turns in each dialogue and to annotate each segment with dialogue act functions and, when applicable, with referent segments (i.e., a segment in a previous turn of the other speaker to which the current segment responds).


In this semi-interval relation set and algebra, events and times are still modelled as temporal intervals, but one is not required to fully specify the bounds of both related intervals in order to describe the relation between them.

when one considers how human annotators cope with the task as it is often cast, it is unsurprising that machine performance is not high.


This continuous aspect of time permits relatively small differences in the alignments of events, which may be at best useless and at worst counterproductive when reasoning.

the linguistic ambiguity in natural language texts can make it hard to choose a single interval relation type.


It is desirable to choose a single relation type when annotating linguistic data in order to keep down the number of choices that annotators need to make, which in turn affects the quality of output.

no work has directly investigated the effect of temporal relation representation design on performance of machine learning systems.


The aim is to show that if we do not evaluate the cross document coreference separately, a system can exploit the evaluation by only focusing on correctly predicting within document coreference links.

mUC (Vilain et al., 1995) A link-level metric, mUC counts the minimum number of edge-insertions or edge-deletions required to obtain the gold clustering (key) from the predicted clustering (response).


In this work, we analyze whether existing CDEC evaluations reflect a system's ability to predict cross document links.

a system can (incorrectly) link two event mentions across topics (or sub-topics), for which it should be appropriately penalized in evaluation.


Similar to prose text, lyrics are a discourse: i.e., they are, typically, a sequence of related lines, rather than an unconnected stream of lines of arbitrary order.

in this study, we preliminarily assume that discourse segment boundaries are indicated by empty lines inserted by lyrics writers.


The main reason behind this may be that the music with the voice induces the emotion.

these features were extracted using the jAudio (McKay et al., 2005) toolkit.


The main reason for such difference may be that the audio and lyrics were annotated by different annotators.

hence, these mistakes were corrected manually.


We also observed that the systems for Hindi songs are quite biased towards the "Class Sa".

we sliced each of the audio files into 60 second clips.


The representation is easily to be biased by some specific words like "Saturday night" in the example.

from the results, we can see that our methods outperform baseline methods.


messages will lead to nonsense response.

a context dependent message should have a larger entropy on responses than a context independent message (see the comparison in Table 2).


The SVM-based pipeline system yields confidence values in the form of hyper-plane distances for each dialogue utterance for every mode, act, subact and also whether a mode switch occurred.

this system is similar to the one presented in Rus et al.


Instead, we utilize the flexibility of MLNs to incorporate the feature-based soft formulas implicitly.

markov logic networks (mLNs) unify first-order logic with markov networks (undirected probabilistic graphical models abbreviated as PGms).


MLNs assume Herbrand semantics, i.e., there is a finite number of objects that can be substituted for the variables in the first-order formulas.

the number of feasible combinations is much lower.


With the use of a "+" sign, we can increase the total number of weights in the MLN and thus induce more complex distributions.

we first label acts in each utterance of the dialogue, then, using the labeled acts, we label subacts, and using both the labeled acts and subacts, we finally label the higher level modes.


To avoid overfitting, a regularization factor is added to Eq.

we used the SwDA dataset to tune all hyperparameters including dropout, decay rate, word embeddings and the number of LSTM layers.


The task of DA classification (sometimes called DA identification) is to attribute one member of a predefined DA to each given utterance.

given the aforementioned problems with RNNs, we use Long Short Term Memory (LSTM), which is a variation of RNNs that is tuned to preserve long-distance dependencies as their default specificity.


As future work we wish to explore learning much simpler abstractions such as entity and concepts.

this symbol map helps in two important ways: it helps preserve the linguistic structure, and at the time of prediction it helps replace unknown symbol with the original word.


6820 conversations were used for training and the remaining 400 were used as a validation set.

σ is the logistic sigmoid function, • is element wise multiplication.


This work is rather different from the conventional works in domain adaptation in that we remove the need to distinguish domains: we have a single model that can handle arbitrary (including unknown) domains.

to evaluate performance of the constrained decoding approach without schema prediction, we compare the performance among possible models, which can handle all domains in table 2.


Insertions and deletions are treated similarly, and the frequency and length effects from Jaccard could also affect this metric.

to evaluate the effect of these string similarity functions, we also implemented a constant similarity function (S(w, w ) = 1, where w and w are words) used in the fourth step, denoting the corresponding embeddings by SWordSS 1 .


To avoid this misrepresentation of rare words, we apply SWordSS embeddings in a language modelling framework.

in Section 3, we evaluate our embeddings on word similarity tasks.


To further compare the performance of the aforementioned language models on rare words, we analyse perplexities of such words (RWPPL) in the test set as a variation of the frequency classes of these words in the training set.

for tasks like spoken term detection, having low perplexities on most frequent set of words is not good enough and hence, we compare LMs on the perplexity of a rare-word based test set.


In Wiktionary, pragmatic labels are specified at the beginning of a sense definition, usually enclosed in parentheses, typed in italics, or separated by a colon.

as the low precision indicates, there are, however, a large number of irrelevant clues that are retrieved by this simple index lookup.


For evaluation data, it will be interesting to cooperate with marketing researchers and copywriters.

we consider our tool helpful for new names, which lack this brand strength.


We build an OR query where at least 25% of the query terms should match (by setting the "minimum should match" parameter), also assigning the scores obtained in the last section to the individual terms in the query.

while DRUID multiwords help to gain much better keyword recognition scores, it did not achieve a better NDCG score on speech transcripts.


A factor graph consists of two layers of nodes, i.e., variable nodes and factor nodes, with links between them.

in our data set, 143389 users are followed by only male users and 119504 users are followed by only female users.


Only scores for noun-verb and noun-adjective combinations have been included with a threshold of LMI ≥ 50.

c2 is lacking since courses at this level are not provided, and it is in general characterized as a near-native language competence.


In the social media domain, Mackinnon and Warren (2006) explore some kind of social features, i.e., the relationship between users to predict a user's age and country of residence in a social network.

finally, Section 6 gives the conclusion and future work.


In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives.

note that the motivation of employing LSTM as our single-perspective learning approach is that LSTM equips with a special gating mechanism that controls access to memory cells and it is powerful and effective at capturing long-term dependencies (Bengio et al., 1994).


Looking into POS diversity and parser label diversity as shown in  Table 4: Accuracy from 10-folds cross validation using all features diversity; 0.37 for syntax labels diversity).

our work also complements prior knowledge gained from studies that predict reviewer expertise based on personal ratings (Amblee & Bui, 2007;Flanagin & Metzger, 2013;Plucker et al., 2009), and/or the online behaviour of reviewers (Liu et al., 2008).


Inspired by recent studies on learning distributed representations of multi-relational data from knowledge graph (Socher et al., 2013), we use a neural tensor network framework for knowledge graph embedding.

the main performance gain results from better similarity between semantically related but lexically different events, thanks to the integration of knowledge.


Instead, in the system enhanced with verbal SPs, in addition to the object, verb "ë †" is also correctly translated into a target string.

in the last section, we conclude with future directions.


Our work consists of two parts: modeling SPs for verbs and incorporating SPs into an SMT system.

section 4 discusses how to deal with unknown words in sPs.


conventional attention model is conducted on the representation of source sentence (fixed after generated) only with reading operation (Bahdanau et al., 2015;Luong et al., 2015a).


We focus on the automated grading of short-answer questions (i.e., assessment questions that require a free-text answer up to two or three sentences in length).

there are few surprises in the feature groups that contribute substantially to model performance: Again, we see a strong reliance on the NGram and similarity features.


Systems then predict answer accuracy as a binary correct-incorrect decision or as a more fine-grained multi-class problem (or even a regression task predicting points).

for us, this led to many false-positive entailment judgments by the TE system, most likely because the longer the Text, the easier it becomes to construct relations between Text and Hypothesis.


For example, the opinion word extracted from the tweet "I don't like the Samsung Galaxy S6."

as the data grows, the patterns of co-occurrences can be sparse and ineffective for prediction.


The model satisfies this goal if it predicts the observed forms at least as well as the baseline model.

phonological constraints and log-linearity Traditional versions of OT do not employ log-linearity, so we work with the MaxEnt OT (MEOT; Goldwater and Johnson (2003)) framework, an extension that connects constraint-based phonology to the general class of log-linear models.


Each constraint has an associated weight, which is always non-positive; no constraint violations can make an output form more likely to be chosen.

given a constraint definition d l , the probability of it producing a violation profile M •l is given by where is the number of exceptions in M •l given candidates {y j } and definition d l , and b is the exception parameter, with larger b penalizing exceptions more strongly.